Maintaining social connections via informal gathering and communication in physical workspaces is challenging. To maintain connections without causing mental strain, we propose “Async Party,” an activity that reconfigures drinking rituals through asynchronicity by combining physical triggers with digital sharing. In this activity, participants record short videos of individual toasts, ask a nearby colleague to record their “cheers” moment, and upload the short video to the dedicated Slack channel. A three-month field deployment revealed that while the filming rule was designed to spark face-to-face interaction at the moment of recording, the encounters it produced were experienced not as procedural steps but as socially meaningful moments in their own right – a social quality that extended beyond our anticipation. Members derived a sense of community vitality not by watching every video, but by peripherally perceiving the accumulation of ritualistic content. Drawing on Research through Design, this study articulates a novel design space for "asynchronous rituals" – configurations in which synchrony and asynchrony are intentionally composed into a recurring temporal cycle – and derives three transferable design strategies for sustaining low-burden belonging in hybrid environments.

AI-assisted writing raises concerns about autonomy and ownership when benefiting writers. Personalization has been proposed as an effective solution while also risking writers’ reliance on AI and behavior shifting. For better personalization design, existing studies rely on interaction and information solely within the writing phase; however, few studies have examined how reading behaviors can inform personalized writing. This study investigates the effects of integrating reading highlights for personalization on AI-assisted writing. A between-subjects study with 46 participants revealed that the personalization condition encouraged participants to produce more highlights. However, highlighting unexpectedly shifted from a sense-making strategy to an instrumental act of "feeding the AI," leading to significant reliance on AI and declines in writers’ sense of autonomy, ownership, and self-credit. These findings indicate personalization risks in AI-assisted writing, emphasize the importance of personalization strategies, and provide design implications.

AI-generated influencers are rapidly gaining popularity on Chinese short-video platforms, often adopting kinship-based roles such as “AI grandchildren” to attract older adults. Although this trend has raised public concern, little is known about the design strategies behind these influencers, how older adults experience them, and the benefits and risks involved. In this study, we combined social media analysis with interviews to unpack the above questions. Our findings show that influencers use both visual and conversational cues to enact kinship roles, prompting audiences to engage in kinship-based role-play. Interviews further show that these cues arouse emotional resonance, help fulfill older adults’ informational and emotional needs, while also raising concerns about emotional displacement and unequal emotional investment. We highlight the complex relationship between virtual avatars and real family ties, shaped by broader sociocultural norms, and discuss how AI might strengthen social support for older adults while mitigating risks within cultural contexts.

Proliferation of misinformation is a globally acknowledged problem. Cognitive Inoculation helps build resistance to different forms of persuasion, such as misinformation. We investigate Conversational Inoculation, a method to help people build resistance to misinformation through dynamic conversations with a chatbot. We built a Web-based system to implement the method, and conducted a within-subject user experiment to compare it with two traditional inoculation methods. Our results validate Conversational Inoculation as a viable novel method, and show how it was able to enhance participants’ resistance to misinformation. A qualitative analysis of the conversations between participants and the chatbot highlighted adaptability, independence, trust and friction as the main factors affecting Conversational Inoculation. We discuss the opportunities and challenges of using Conversational Inoculation to combat misinformation. Our work contributes a timely investigation and a promising research direction in scalable ways to combat misinformation.

In difficult workplace email conversations, such as layoffs or resource negotiations, the absence of nonverbal cues can exacerbate negative emotions experienced by recipients. While existing tools support senders in refining tone, there is little support for processing emotionally intensive content from the receivers’ side. This study investigated the use of large language models that added positive or neutral reframings, written in either first or third person, to original emails, with the aim of helping recipients view difficult conversations in a different light. In a controlled study with 132 participants, positive reframing reduced receivers’ negative emotions and was rated as more helpful than neutral reframing, regardless of narrative perspective. Although reframing type did not significantly change conflict management behaviors, positive reframing led to fewer power-related words in interpretations of the email. These findings highlight opportunities and challenges for designing AI as a social buffer to facilitate difficult conversations online.

Critical reading is a primary way through which researchers develop their critical thinking skills. While exchanging thoughts and opinions with peers can strengthen critical reading, junior researchers often lack access to peers who can offer diverse perspectives. To address this gap, we designed an in-situ thought exchange interface informed by peer feedback from a formative study (N=8) to support junior researchers’ critical paper reading. We evaluated the effects of thought exchanges under three conditions (no-agent, single-agent, and multi-agent) with 46 junior researchers over two weeks. Our results showed that incorporating agent-mediated thought exchanges during paper reading significantly improved participants’ critical thinking scores compared to the no-agent condition. In the single-agent condition, participants more frequently made reflective annotations on the paper content. In the multi-agent condition, participants engaged more actively with agents’ responses. Our qualitative analysis further revealed that participants compared and analyzed multiple perspectives in the multi-agent condition. This work contributes to understanding in-situ AI-based support for critical paper reading through thought exchanges and offers design implications for future research.

As AI writing support becomes ubiquitous, the question of how disclosing its use affects reader perception remains critical and underexplored. We conducted a controlled study with 261 participants to examine how disclosing varying levels of AI involvement shifts perceptions of the author across six distinct communicative acts. Our analysis of 990 evaluations reveals that disclosure generally erodes perceived trustworthiness, caring, competence, and likability, with the most precipitous declines observed in social and interpersonal writing. A thematic analysis of participant feedback attributes these negative shifts to a perceived loss of human sincerity, diminished authorial effort, and the contextual inappropriateness of AI. Notably, however, we find that higher AI literacy mitigates these negative perceptions, leading to greater tolerance or even appreciation for AI assistance. Our results highlight the nuanced social dynamics of AI-mediated authorship and inform design implications for transparent, context-sensitive writing systems that better preserve trust and authenticity.

Premenstrual syndrome (PMS) is a prevalent disorder among women, often exacerbated by a lack of peer support due to associated stigmatization.Drawing inspiration from the established benefits of group therapy, particularly the sense of belonging it fosters, we developed a multi-chatbot group motivational interviewing system.The system consists of a facilitator bot and two peer bots, and simulates a group counseling environment for PMS management using Large Language Models (LLMs).We conducted a study with 63 participants and divided them into three conditions (no intervention, 1-on-1 chatbot, group chatbots) over two menstruation cycles for evaluation.Our findings revealed that participants in the group chat condition exhibited higher levels of engagement and language convergence with the chatbots.These participants were also able to engage in social learning and demonstrated motivation in coping through interactions with the chatbots.Finally, we discuss design implications for multi-chatbot interactions in supporting mental health.

Psychotherapy, such as cognitive-behavioral therapy (CBT), is effective in treating various mental disorders. Technology-facilitated mental health therapy improves client engagement through methods like digitization or gamification. However, these innovations largely cater to individual therapy, ignoring the potential of group therapy-a treatment for multiple clients concurrently, which enables individual clients to receive various perspectives in the treatment process and also addresses the scarcity of healthcare practitioners to reduce costs. Notwithstanding its cost-effectiveness and unique social dynamics that foster peer learning and community support, group therapy, such as group CBT, faces the issue of attrition. While existing medical work has developed guidelines for therapists, such as establishing leadership and empathy to facilitate group therapy, understanding about the interactions between each stakeholder is still missing. To bridge this gap, this study examined a group CBT program called the Serigaya Methamphetamine Relapse Prevention Program (SMARPP) as a case study to understand stakeholder coordination and communication, along with factors promoting and hindering continuous engagement in group therapy. In-depth interviews with eight facilitators and six former clients from SMARPP revealed the motivators and demotivators for facilitator-facilitator, client-client, and facilitator-client communications. Our investigation uncovers the presence of discernible conflicts between clients' intrapersonal motivation as well as interpersonal motivation in the context of group therapy through the lens of self-determination theory. We discuss insights and research opportunities for the HCI community to mediate such tension and enhance stakeholder communication in future technology-assisted group therapy settings.

Interpersonal communication often involves navigating social challenges like managing expectations and conveying intentions. In practical contexts like productivity and navigation of social boundaries, agent- and AI-mediated communication (AIMC) has served as an effective social intermediary. Communication within personal relationships, especially between intercultural friends from different backgrounds, can also face significant challenges, such as miscommunication and expressive suppression. However, AIMC remains underutilized in the context of established personal relationships due to ethical concerns about agency and authenticity. We propose designing openly-interpretable AIMC output as an augmented context cue to reduce its active social involvement, balancing AI support with preservation of relational agency. We developed and evaluated Poet-Weaver, a Discord text chat plugin that presents AI-generated insights on user conversations in a stylized, openly-interpretable way to encourage reflection on communication challenges. We conducted a mixed-methods study with 30 intercultural friend pairs to assess Poet-Weaver's impact. Findings showed Poet-Weaver effectively helped participants address communication failures, although AIMC still influenced users' behavior even with openly-interpretable output. We recommend future uses of AIMC that support transformative, positive relationship changes while preserving individual responsibility and identity within relationships.

Negative psychological effects from losing in online competitive games, such as frustration and reduced self-evaluation, can lead to player disengagement. Previous research using puzzle tasks has shown that providing losers with positive feedback on their performance after the task can enhance their intrinsic motivation. However, such findings were demonstrated in analog settings where feedback was delivered verbally by a human experimenter, and it remains unclear whether similar effects hold in digital environments such as online competitive games. This study investigates a feedback intervention using a virtual agent on a computer, aimed at mitigating such negative impacts and promoting sustained intrinsic motivation. In a controlled experiment with 45 participants, we compared three types of feedback: No Feedback (NF), Feedback for the Participant (FP), which included praise for the player’s good moves, and Feedback for Both (FB), which also praised the opponent and emphasized social connection. The results indicated that providing feedback, whether it was directed at the player alone or at both the player and the opponent, significantly increased perceived competence, autonomy, intrinsic motivation, and the intention to play again compared to not providing any feedback at all. We discuss the potential of personalized, praise-based feedback in sustaining healthy motivation during competitive gameplay.

Trust in agents within Virtual Reality is becoming increasingly important, as they provide advice and influence people's decision-making. However, previous studies show that encountering speech recognition errors can reduce users' trust in agents. Such errors lead users to ignore the agent's advice and make suboptimal decisions. While agents can offer an apology to repair trust, its effectiveness is often limited because it fails to fully repair the original level of trust. Therefore, we examined the use of social touch, a social interaction involving physical interaction between users and the virtual agent, to enhance the effect of an apology on trust repair and to increase the acceptability of its advice. In a controlled experiment (N=24), participants experienced a robotic arm touching the back of their hands while interacting with the agent before decision-making. The results showed that social touch did not repair participants' trust in agents. However, participants were more likely to accept the agent's advice when they experienced touch with physical feedback, regardless of the level of trust in the agent. We discuss the role of presenting physical haptic feedback and its influence on human-agent interactions in VR.

Rekindling old social bonds with individuals who were once a part of our lives but have since faded away is crucial for our well-being. Such connections with dormant ties help us overcome loneliness and provide social support. Recently, virtual reality (VR) emerged as a promising tool for facilitating social interactions, such as online gatherings for formal or casual activities. VR can offer immersive and shared experiences, facilitating genuine connections between people. This provides a unique advantage over traditional computer-mediated communication methods. However, while prior research has explored how VR can aid in forming new social connections, its potential to reconnect dormant ties is largely unexplored. This paper aims to bridge this gap by examining how different features of VR, specifically avatar appearance and virtual environments, influence reactivations of dormant ties. We conducted an experiment involving 24 dyads to investigate the effect of different avatar-self similarities and virtual environments on the perceptions and interactions between dormant ties. Our findings indicate that avatars resembling oneself and dormant ties promote social closeness. Familiar virtual environments evoke shared memories, while unfamiliar ones stimulate more conversations. We discuss the impact of VR features on reconnecting dormant ties and provide implications for re-connecting relationships in VR.

Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems.

The limited nonverbal cues and spatially distributed nature of remote communication make it challenging for unacquainted members to be expressive during social interactions over video conferencing. Though it enables seeing others' facial expressions, the visual feedback can instead lead to unexpected self-focus, resulting in users missing cues for others to engage in the conversation equally. To support expressive communication and equal participation among unacquainted counterparts, we propose SealMates, a behavior-driven avatar in which the avatar infers the engagement level of the group based on collective gaze and speech patterns and then moves across interlocutors' windows in the video conferencing. By conducting a controlled experiment with 15 groups of triads, we found the avatar's movement encouraged people to experience more self-disclosure and made them perceive everyone was equally engaged in the conversation than when there was no behavior-driven avatar. We discuss how a behavior-driven avatar influences distributed members' perceptions and the implications of avatar-mediated communication for future platforms.

During video conferencing, technical issues such as network impairments can unexpectedly hinder remote collaboration for distributed teams. However, it remains unclear how such issues affect the impression formation process between unacquainted interlocutors in situations like job interviews or kick-off meetings. Having first encounters online without prior in-person interaction has become prevalent nowadays. Therefore, examining the impact of technical issues on remote unacquainted interlocutors and exploring design solutions to reduce the negative impact of technical issues on the impression is essential in informing the design of future communication systems. With three controlled experiments simulating an online job interview, we discovered that technical issues made people give low credibility ratings to remote job applicants (Study 1). We further examined two intervention approaches to reduce the negative impact of technical issues in video conferencing, including a virtual agent represented in a human-like avatar to actively take responsibility for the technical issues (Study 2), and forewarning messages to inform people about the technical issues in video conferencing (Study 3). Results demonstrated that introducing an agent that represents a communication system to take responsibility for technical issues actively could reduce the responsibility people assign to remote job applicants, but without increasing their positive credibility rating for remote job applicants. Furthermore, showing forewarning messages, which explicitly made people aware the cause of technical issues was unidentifiable and its potential negative impacts on impression formation, enabled people to rate the credibility of the remote counterpart without being influenced by technical issues. We discussed how interventions can be designed to mitigate negative attribution outcomes when encountering uncontrollable technical issues in computer-mediated communication.

When receiving text messages from unacquainted colleagues in fully remote workplaces, insufficient mutual understanding and limited social cues can lead people to misinterpret the tone of the message and further influence their impression of remote colleagues. Emojis have been commonly used for supporting expressive communication; however, people seldom use emojis before they become acquainted with each other. Hence, we explored how changing facial expressions in profile pictures could be an alternative channel to communicate socio-emotional cues. By conducting an online controlled experiment with 186 participants, we established that changing facial expressions of profile pictures can influence the impression of the message receivers toward the sender and the message valence when receiving neutral messages. Furthermore, presenting incongruent profile pictures to positive messages negatively affected the interpretation of the message valence, but did not have much effect on negative messages. We discuss the implications of affective profile pictures in supporting text-based communication.

Video-based learning is widely adopted by online learners, yet, learning experience and quality may be negatively affected by asynchronous and remote natures of video-based learning. As note-taking is a common practice employed by video-based learners and is known to be an effective way to trigger active construction and processing of knowledge, yet as a meta-skill, it is challenging to most learners. In this study, we aim to approach the goal of providing cognitive and social scaffolds to video-based learners by structuring their note-taking process. We presented and evaluated structured note-taking systems designed for learners in two contexts, namely, individual learning context and social learning context. With an online controlled study involving 43 participants, we compared the structured note-taking systems with two baseline systems (for individual learning and social learning contexts respectively) and found that structured note-taking significantly improved certain aspects of video-based learning such as and higher cognitive engagement and lower distraction. We discussed our results to inform the design, iteration, and adoption of note-taking tools in video-based learning.

Workers tend to make inferences about one another's commitment and dedication to work depending on what cues are available to them, affecting worker relationships and collaboration outcomes. In this work, we investigate how remote work affects workers' perceptions of their colleagues with different levels of social connectivity, commonly referred to as strong ties and weak ties. When working remotely, workers' perceptions of weak ties may suffer due to the lack of in-person interaction. On the other hand, workers' inferences about their strong ties may also be impacted by losing richer communication cues, even though they had more connections with their strong ties than weak ties. This study explores how remote workers make inferences about engagement levels of and willingness to collaborate with weak ties compared to strong ties. We used a mixed-methods approach involving survey data, experience sampling, and in-depth interviews with 20 workers from different companies in Taiwan. Results showed that workers depended on one-on-one synchronous tools to infer the engagement level of strong ties but used group-based communication tools to infer the engagement level of weak ties. Interestingly, the absence of cues in remote workplaces exacerbated prior impressions formed in the physical office. Furthermore, remote work led workers to develop polarized perceptions of their respective ties. We discuss how characteristics of computer-mediated communication tools and interaction types interplay to affect workers' perceptions of remote colleagues and identify design opportunities for helping remote workers maintain awareness of weak ties.

Insufficient social cues between distributed learners in online learning could result in lack of engagement and social bonds. With the development of wearable sensing, sharing physiological data can be used to enhance mutual understanding and connectedness among sharers. Our work aims to explore the potential of sharing heart rate (HR) and heart rate variability (HRV) collected from distributed learners to enhance their online learning experiences. We implemented a physiological streaming system and conducted a field study with 11 learners in online classes. This paper describes the study and discusses our interview findings by contrasting the influence of visualized collective physiological data from viewpoints of data contributors and viewers. Our exploratory results suggest streaming collective HR and HRV from multiple distributed learners could be used in online classes to improve engagement and sense of community.

In workplaces, knowledge has the property of being individually kept and collectively shared. It is distributed and embedded among members but needs to be transferred and pooled through knowledge networks for successful collaboration. In order to understand and embody such networks, we employed a mixture of field observations, social network analysis, and in-depth interviews to examine the practices of knowledge transfer in a local financial institute (N=32). Results from the analyses show that there is a discrepancy between inbound and outbound knowledge transfer, and also suggest that expertise or social relation cannot solely account for members' decision on whom to ask questions and share knowledge. We also found that scaffolding workers to connect to the right person with metaknowledge of the knowledge network is important. Our findings point to research and design opportunities to support knowledge transfer at the collaborative level within a knowledge community.

To accomplish collaborative work, collaborators need to know one another's states of work for coordination. Similarly, in situations of knowledge transfer where experts need to instruct novices by passing implicit knowledge and personal experience about the task, one challenge lies in the knowledge gap between experts and novices. Both parties need to gain an awareness of each other's state of task knowledge for effective knowledge transfer. In this paper, we aim to investigate how online knowledge transfer is supported by a design called knowledge transparency of a shared document workspace, which allows explicitly annotating and sharing ones' knowledge state to make it visible to their collaborators. We conducted a laboratory study to examine how knowledge transparency affects knowledge transfer among experts and novices using an image editing task over an online document. The results shed light on how to support knowledge co-construction and communication for the purpose of knowledge transfer through interface design.

Knowledge transfer in workplace, in which job-related knowledge and skills are transferred from a more experienced worker to a novice (e.g., a new employee), is common and crucial for organizations to keep transactive memory effective and avoid productivity loss. Organizational and other constraints may require expert-to-novice knowledge transfer to occur through the mediation of external artifacts (e.g., documents) or a third-party individual (e.g., human moderator), raising the need to understand how properties of such mediation influence the process and outcome of knowledge transfer. In my doctoral study, I aim to gain deeper understanding and implications for knowledge transfer designs.